COMPUTER SIMULATION OF NEURAL NETWORK CONTROL SYSTEM FOR CO_2 WELDING PROCESS

In this paper, neural network control systems for decreasing the spatter of CO2 welding have been created. The Generalized inverse Learning Architecture(GILA), the SPecialized inverse Learning Architecture(SILA)-I & H and the Error Back Propagating Model(EBPM) are adopted respectively to simulate the static and dynamic welding control processes. The results of simulation and experiment show that the SILA-I and EBPM have betted properties. The factors affecting the simulating results and the dynamic response quality have also been analyzed.

Application of Artificial Neural Networks Based Monte Carlo Simulation in the Expert System Design and Control of Crude Oil Distillation Column of a Nigerian Refinery

This research work investigated comparative studies of expert system design and control of crude oil distillation column (CODC) using artificial neural networks based Monte Carlo (ANNBMC) simulation of random processes and artificial neural networks (ANN) model which were validated using experimental data obtained from functioning crude oil distillation column of Port-Harcourt Refinery, Nigeria by MATLAB computer program. Ninety percent (90%) of the experimental data sets were used for training while ten percent (10%) were used for testing the networks. The maximum relative errors between the experimental and calculated data obtained from the output variables of the neural network for CODC design were 1.98 error % and 0.57 error % when ANN only and ANNBMC were used respectively while their respective values for the maximum relative error were 0.346 error % and 0.124 error % when they were used for the controller prediction. Larger number of iteration steps of below 2500 and 5000 were required to achieve convergence of less than 10-7?for the training error using ANNBMC for both the design of the CODC and controller respectively while less than 400 and 700 iteration steps were needed to achieve convergence of 10-4?using ANN only. The linear regression analysis performed revealed the minimum and maximum prediction accuracies to be 80.65% and 98.79%;and 98.38% and 99.98% when ANN and ANNBMC were used for the CODC design respectively. Also, the minimum and maximum prediction accuracies were 92.83% and 99.34%;and 98.89% and 99.71% when ANN and ANNBMC were used for the CODC controller respectively as both methodologies have excellent predictions. Hence, artificial neural networks based Monte Carlo simulation is an effective and better tool for the design and control of crude oil distillation column.

Neural Network-Powered License Plate Recognition System Design

The development of scientific inquiry and research has yielded numerous benefits in the realm of intelligent traffic control systems, particularly in the realm of automatic license plate recognition for vehicles. The design of license plate recognition algorithms has undergone digitalization through the utilization of neural networks. In contemporary times, there is a growing demand for vehicle surveillance due to the need for efficient vehicle processing and traffic management. The design, development, and implementation of a license plate recognition system hold significant social, economic, and academic importance. The study aims to present contemporary methodologies and empirical findings pertaining to automated license plate recognition. The primary focus of the automatic license plate recognition algorithm was on image extraction, character segmentation, and recognition. The task of character segmentation has been identified as the most challenging function based on my observations. The license plate recognition project that we designed demonstrated the effectiveness of this method across various observed conditions. Particularly in low-light environments, such as during periods of limited illumination or inclement weather characterized by precipitation. The method has been subjected to testing using a sample size of fifty images, resulting in a 100% accuracy rate. The findings of this study demonstrate the project’s ability to effectively determine the optimal outcomes of simulations.

Adaptive control of parallel manipulators via fuzzy-neural network algorithm

This paper considers adaptive control of parallel manipulators combined with fuzzy-neural network algorithms （FNNA）. With this algorithm, the robustness is guaranteed by the adaptive control law and the parametric uncertainties are eliminated. FNNA is used to handle model uncertainties and external disturbances. In the proposed control scheme, we consider modifying the weight of fuzzy rules and present these rules to a MIMO system of parallel manipulators with more than three degrees-of-freedom （DoF）. The algorithm has the advantage of not requiring the inverse of the Jacobian matrix especially for the low DoF parallel manipulators. The validity of the control scheme is shown through numerical simulations of a 6-RPS parallel manipulator with three DoF.

Dynamic Coordination of Uncalibrated Hand/Eye Robotic System Based on Neural Network

A nonlinear visual mapping model is presented to replace the image Jacobian relation for uncalibrated hand/eye coordination. A new visual tracking controller based on artificial neural network is designed. Simulation results show that this method can drive the static tracking error to zero quickly and keep good robustness and adaptability at the same time. In addition, the algorithm is very easy to be implemented with low computational complexity.

Study on Intelligent Control and 3D Real-time Distributed Animation Simulation for Super-maneuver Attack of the New Generation Fighter

The super-maneuver flight performance has a very high tactical value, and the development of this tactical value has great significance. A discussion is devoted to the study of intelligent control methods and technologies of real-time distributed 3-dimensional animation simulation for the super-maneuverable attack of new generational fighter in this paper. A flight control system of super-maneuver is reconstructed by adopting three layers BP neural networks of number 3, and the fire/flight coupler is designed by introducing a fuzzy control rule whose universe of discourse and gain are regulated adaptively on the line. Furthermore, a new method of real-time distributed 3-dimensional animation simulation is put forward, and a real-time distributed 3-dimensional animation simulation tool platform is constructed in this paper. The simulation result is lifelike, perceivable directly and useful.

Application of simulated annealing genetic algorithm-optimized back propagation(BP)neural network in fault diagnosis

Optimal weights are usually obtained in neural network through a fixed network conformation,which affects the practicality of the network.Aiming at the shortage of conformation design and weight training algorithm in neural network application,the back propagation(BP)neural network learning algorithm combined with simulated annealing genetic algorithm(SAGA)is put forward.The multi-point genetic optimization of neural network topology and network weights is performed using hierarchical coding schemes and genetic operations.The simulated annealing mechanism is incorporated into the Genetic Algorithm(GA)to optimize the design and optimization of neural network conformation and network weights simultaneously.The SAGA takes advantage of GA excellent ability in grasping the overall ability of the search process,also uses the SA algorithm to control the convergence of the algorithm to avoid premature phenomenon.The fault diagnosis of one certain on-board electrical control box of helicopter and one certain flight control box of aircraft autopilot were used as a test platform to simulate the algorithm.The simulation conclusions reveal that the algorithm has good convergence rate and high diagnostic accurateness.

Backstepping sliding mode control with self recurrent wavelet neural network observer for a novel coaxial twelve-rotor UAV

The robust attitude control for a novel coaxial twelve-rotor UAV which has much greater payload capacity,higher drive capability and damage tolerance than a quad-rotor UAV is studied. Firstly,a dynamical and kinematical model for the coaxial twelve-rotor UAV is designed. Considering model uncertainties and external disturbances,a robust backstepping sliding mode control( BSMC) with self recurrent wavelet neural network( SRWNN) method is proposed as the attitude controller for the coaxial twelve-rotor. A combinative algorithm of backstepping control and sliding mode control has simplified design procedures with much stronger robustness benefiting from advantages of both controllers. SRWNN as the uncertainty observer is able to estimate the lumped uncertainties effectively.Then the uniformly ultimate stability of the twelve-rotor system is proved by Lyapunov stability theorem. Finally,the validity of the proposed robust control method adopted in the twelve-rotor UAV under model uncertainties and external disturbances are demonstrated via numerical simulations and twelve-rotor prototype experiments.

基于深度神经网络的电液伺服泵控系统健康评估研究

电液伺服泵控系统具备功重比高、响应快等优点,在多领域得到广泛应用,但如何针对该系统开展更有效健康评估,进一步保障系统的安全性和可靠性成为必须面对的问题。按照明确原理、建立数学模型、建立仿真模型、仿真分析的思路针对健康评估方法开展研究,提出油液体积含气量、气隙磁密、泄漏系数3个健康评估指标并确定阈值,构建了LGA(LSTM-GRNN-ANN)深度神经网络健康评估方法并进行仿真分析,结果显示该方法准确率约为97.48%,比LSTM、GRNN健康评估方法具有更高的准确率,为继续深入开展电液伺服泵控系统健康评估的研究提供了理论支持。

连续油管井下牵引器自适应控制设计及仿真

当连续油管在水平段受到过大摩擦阻力而无法达到预定位置或出现自锁时,需要采用牵引器来增加连续油管的极限下入深度,使连续油管达到预定作业位置。针对井下牵引器控制系统的调节速度与误差抑制问题,设计了基于模糊神经网络PID控制策略,调整了其模糊规则;最后,在阶跃响应和正弦信号下进行Simulink仿真,验证其动态性能与静态性能。通过实验分析,结果表明:在阶跃响应下的上升时间为1.8s,系统无超调,调节时间为2s;正弦响应的最大滞后时间在0.3s左右,最大幅值误差在0.05kPa,幅值误差小,快速性好,跟随性能优越。基于模糊神经网络PID控制超调量减少51.3%,调节时间减少3.6s,可以更好的控制牵引器,使连续油管更加稳定准确快速的达到预定作业位置。

采用改进BP-PID控制的机器人避障仿真研究

针对移动机器人避障过程中行驶路径长、寻路速度慢等问题,提出了一种改进反向传播-比例-积分-微分(BP-PID)控制器,并对移动机器人避障效果进行仿真验证。利用移动机器人在二维坐标系的避障简图,得出了移动机器人运动方程式。引用比例-积分-微分(PID)控制器和3层BP神经网络结构,利用BP神经网络的学习能力调整PID控制器参数。引用粒子群算法进行改进,通过改进粒子群算法在线优化BP-PID控制器,确保移动机器人BP-PID控制器收敛于全局最优值,从而使移动机器人避障效果更好。在不同环境中,采用Matlab软件对移动机器人避障效果进行仿真,比较改进前和改进后的移动机器人避障效果。结果显示:在不同环境中,改进前和改进后的BP-PID控制器均能使移动机器人安全地躲避障碍物;但是采用改进的粒子群算法优化BP-PID控制器,可以使移动机器人运动路径更短,迭代次数更少,搜索时间更短。采用改进BP-PID控制器,能够提高移动机器人避障过程中寻路速度,缩短行驶路径,效果更好。

基于BP神经网络算法的粉碎机自适应控制系统设计

针对饲料加工行业中锤片式粉碎机控制系统存在启动时间长、响应速度慢及负载变化时出现的稳定性差等问题,提出了一种基于BP神经网络算法PID控制方法。首先,建立变频器和饲料粉碎机驱动电机组合系统传递函数的参考模型,并对其进行稳定性分析。然后在分析常规PID和模糊PID控制算法的基础上,将自适应神经网络算法PID应用到饲料粉碎机驱动系统的控制过程当中。通过搭建饲料粉碎机控制电机的仿真模型,利用MATLAB软件中的Simulink图形化编程功能对其进行仿真分析,并基于LABVIEW软件搭建了粉碎机测控系统试验平台进行实验测试分析。结果表明:对于饲料粉碎系统所给定的速度参考模型,设计的BP神经网络PID控制器能够实现较好的自适应追踪,对阶跃信号的响应更加迅速、超调更小,抗干扰能力更强。设计的自适应控制器能够根据工况变化自动调节PID参数,吨料电耗平均降低5.16%、生产率平均提高2.08%,对粉碎机主轴转速的控制更加精确,误差更小,兼具了较高的控制精度和较强的鲁棒性,满足饲料粉碎机驱动系统的自适应控制要求。

基于神经网络的轮式移动机器人非线性模型预测控制研究

针对轮式移动机器人受到障碍物干扰导致运动轨迹跟踪误差较大问题,提出了神经网络非线性模型预测(NN-NMP)控制方法,并对轮式移动机器人避障效果进行仿真验证。建立了轮式移动机器人运动模型,并且定义了机器人运动方程式。设计非线性模型预测控制方法,引用神经网络算法,通过训练多层神经网络对非线性模型预测控制误差进行逼近,从而使轮式移动机器人控制系统具有更好的避障能力。设置3种不同环境,利用Matlab软件对轮式移动机器人避障结果进行仿真,对比和分析轮式移动机器人采用2种控制方法的输出结果。结果显示:在无障碍物环境中,采用传统比例-积分-微分(PID)控制方法和NN-NMP控制方法,轮式移动机器人均能较好地按照期望轨迹进行移动。在有障碍物环境中,采用传统PID控制方法,轮式移动机器人虽然能够躲避障碍物,但是跟踪误差较大。采用NN-NMP控制方法,轮式移动机器人不仅能够躲避障碍物,而且跟踪误差相对较小。采用NN-NMP控制方法,能够降低轮式移动机器人跟踪误差,具有较好的避障能力。

基于血流动力学效应优化的体外反搏闭环反馈调控研究

目的 本文旨在实现针对不同的患者个体,以较优的血流动力学效应为目标,实现个性化的体外反搏(enhanced external counterpulsation,EECP)的闭环反馈调控数值模拟。方法 通过集中参数模型模拟研究EECP反搏模式对不同的适应症的影响机制,根据PID控制系统的反馈调节原理,将其应用于体外反搏的研究中,把血液循环系统的闭环式0维集中参数模型作为被控对象,用于个性化体外反搏血流动力学仿真。结合体外反搏局部血流动力学效应评价模型为不同适应症(冠心病或脑卒中)患者设定血流动力学效应优化的个性化目标值,根据设定目标值和当前血流动力学指标之间的误差,通过设置适当的增益系数与误差进行运算来调节不同反搏模式(压力幅值或加压时长)使当前血流动力学指标达到设定目标值。结果 反搏压力对于冠状动脉血流动力学指标的增加更加明显。加压时长对于脑动脉的血流动力学效应的影响最为显著。因此对于冠心病患者设定优化目标值舒张压/收缩压(D/S),通过反馈调节反搏压力使当前D/S达到设定D/S,最终使它们之间误差e的绝对值降低至0.06(均<5%),并得到此时的压力幅值。对于脑卒中患者设定目标值脑血流量(cerebral blood flow,CBF),调节系统输入加压时长减小误差,控制系统的输出当前CBF达到设定CBF,使误差e的绝对值降低至0.06(均<5%)并得到此时的加压时长。结论 数值模拟中血流动力学指标达到优化目标值时应施加的压力幅值和加压时长可为临床体外反搏优化治疗提供理论依据。

基于CTNN算法的智能小车路径跟踪控制研究

针对目前路径跟踪控制检测方法精度低、实时性能差的问题,提出一种基于深度机器学习的构建树形神经网络CTNN(Constructing Tree shaped Neural Net)的深度学习算法。该算法通过深度机器学习,构建针对性强的学习集,同时在模型车中实现。将传统机器学习算法与文章所提出的算法在相同行驶条件下的实时响应进行比较,仿真结果表明,CTNN算法在恶劣的行驶环境中,实时性、鲁棒性均得到一定程度的提高。

基于扰动观测器的双容液位系统RBF神经网络滑模控制

针对双容液位系统存在的外部扰动、模型参数不确定等问题,设计了一种基于非线性扰动观测器(nonlinear disturbance observer,NDOB)的径向基函数神经网络滑模控制(radial basis function neural network sliding mode control,RNNSMC)方法。建立双容液位系统数学模型,采用积分型滑模面来提高系统的鲁棒性,在常规积分滑模控制的基础上,通过RBF神经网络(RBF neural network,RNN)对系统的非线性函数进行逼近,并设计非线性扰动观测器估计外部扰动,选用Lyapunov稳定性判据证明了控制策略的闭环稳定性。仿真结果表明,所提控制策略与积分滑模控制(integral sliding mode control,ISMC)方法相比,不需要精确的数学模型,且控制精度更高,抗干扰能力更强。

基于RBF神经网络整定PID的电液比例系统位置控制研究

针对凿岩机械臂的电液比例系统位置控制精度问题,提出了一种基于径向基函数(RBF)神经网络整定PID的电液比例系统位置控制方法。首先,在AMESim中搭建了阀控非对称液压缸的电液比例系统简化模型,设置了各个模块的参数;然后,利用MATLAB/Simulink搭建了系统闭环控制模型,通过不断更新RBF网络模型并修正PID参数,实现了基于RBF神经网络整定PID的电液比例系统位置控制目的;结合AMESim搭建的电液比例系统模型和Simulink下搭建的控制器进行了联合仿真;最后,基于凿岩台车机械臂实验平台,进行了电液比例系统位置控制实验。仿真结果表明:在受到外部干扰的情况下,RBF神经网络整定PID控制系统能够在0.3 s内控制活塞杆重新运行至目标位置,平均响应时间为1.5 s,位置精度误差不超过5 mm。实验结果表明:与常规PID控制方法相比,RBF神经网络整定PID控制活塞杆位置精度误差降低了75%,位置精度误差在工程实际要求的10 mm范围以内,因此,RBF神经网络整定PID算法可以有效提高电液比例系统的位置控制精度,满足凿岩机械臂实际工作中对电液比例系统位置精度的控制要求。

基于力失衡的二维主动磁悬浮驱动方法分析与测试

提出一种基于力失衡的磁悬浮驱动系统,并对外磁场主动驱动设计一种神经网络PID(Proportion Integration Differentiation)控制方法,可以有效应对非线性、模型参数不明确的复杂驱动系统。实验结果表明,该控制算法解决了基于模型控制方法中被控对象与稳态工作点之间偏差增大、跟踪性能迅速恶化等问题,实现了磁性小球在二维空间中的稳定悬浮与高精度可控运动。可应用于工业领域细长管道内壁非破坏式质量检测、管道内部清淤和医疗领域血管、肠腔内的检查和手术。

基于流量预测的波纹管泵流量脉动抑制控制方法

随着晶圆内部线宽减小,在晶圆制造的湿法清洗工艺中对超纯水杂质颗粒的数量和直径提出了更严格的要求,以提高晶圆生产的产量,因此,对作为流控部件的波纹管泵(BsP)有更高的要求。针对波纹管泵运行过程中流量脉动导致的超洁净水颗粒杂质增加和晶圆良品率下降的问题,提出了一种基于神经网络流量预测模型的流量脉动抑制控制方法。首先,阐述了波纹管泵的工作原理和流量脉动产生的原因;然后,介绍了波纹管泵流固耦合模型的搭建方法,使用神经网络流量预测模型建立了流量脉动前馈控制的理论方法;最后,使用COMSOL仿真进行了流量预测模型验证研究,基于Simulink仿真平台开展了前馈控制应用于流量脉动抑制的验证研究。研究结果表明:在流量为[3.39,5.01]L/min时,预测的绝对流量误差范围为[8.0×10^(-1),1.6×10^(-1)]L/min;当流量为5.0 L/min时,最大流量脉动可从1.6 L/min降至4.0×10^(-1) L/min。在仿真验证中,该方法对波纹管泵脉动抑制有显著的效果,还可以有效提高波纹管的性能。

喷雾测量系统坐标架三维位移控制研究

为解决航天发动机试验中,通过三维PDA(Phase Doppler Anemometry)测量系统研究燃料喷注器雾化效果中对测量点精确定位的问题,采用自适应模糊神经网络PID控制算法实现对测量系统三维坐标架的步进电机进行控制。建立了一个步进驱动电机进行位置控制的传递函数模型,通过Matlab的Simulink仿真平台对步进驱动电机位移的PID控制、模糊PID控制及模糊神经网络PID控制的三种算法进行仿真。模拟实验结果表明:模糊神经网络PID控制将响应时间提升到0.05 s,并且大幅减小超调。相比于传统PID此方法可大幅提高坐标架定位的速度和精度。